| --- |
| license: apache-2.0 |
| tags: |
| - pytorch |
| - medical imaging |
| - survival analysis |
| - time-to-event |
| - resnet |
| - CT scans |
| model-index: |
| - name: ResNet-TTE |
| results: [] |
| --- |
| |
| # DenseNet Checkpoint |
|
|
| This is a PyTorch Lightning `.ckpt` checkpoint for a ResNet model trained on chest CT images with TTE objective. |
|
|
| ## Usage |
|
|
| A quickstart script is below. |
|
|
| ```python |
| import torch |
| from src.networks import resnet152 |
| model = resnet152(n_input_channels=1, num_classes=2).to(device) |
| state_dict = torch.load( |
| loadmodel_path, map_location=f"cuda:{torch.cuda.current_device()}" |
| ) |
| model.load_state_dict(state_dict) |
| ``` |
|
|
| For detailed instructions please follow the [README in Github repo](https://github.com/som-shahlab/tte-pretraining/tree/main?tab=readme-ov-file#evaluation). |
